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Mill Wide Information Systems . In a mill there are typically many independent DCS’s, PLC’s, Data Historians, etc. Several companies offer mill wide data systems (PI, DataParc,CIM21).
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Mill Wide Information Systems • In a mill there are typically many independent DCS’s, PLC’s, Data Historians, etc. • Several companies offer mill wide data systems (PI, DataParc,CIM21). • Mill wide information systems combine data from various sources in one central location and provide tools for accessing and utilizing data
PI System • Set of software modules for plant-wide monitoring and analysis. • The data archive is the foundation of the system. It handles the collection, storage, and retrieval of time oriented numerical and string data. It also acts as a data server for Microsoft Windows-based client applications.
PI-API PI Distributed Data Collection Home node Client node * PI-CM Batch PI-PB PI Base Package API API API PI-DL Profile Windows NT or UNIX API API Windows 3.1, 95, or NT PINet node * PINet VMS Interface node PIonPINet node NT or UNIX * PIonPINet Virtual Paper Machine VMS * Sources of data, such as DCS’s, PLC’s, lab systems, process models, etc.
Data Storage • Memory wasn’t always cheap. • Algorithms were devised for data storage. • Enough data is stored to reconstruct original data. • Amount of data compression can be specified as required.
Process Testing • Bump tests or PRBS (pseudo random binary steps) are used to generate process data for process modeling. • Idea is to start at steady-state and bump one input variable while holding the others constant and measure its effect on all output variables. Can be difficult in mill situation.
Process Testing • Bumps must be large enough to differentiate effects on output from noise in output measurement. They should be in the typical operating range of the process. • You should take samples at a regular interval. This is usually determined by availability of output measurement.
Paper Machine Testing and Modeling • Use PI Process Book to view and manipulate papermachine. • Retrieve data for analysis using PI Datalink which is an Excel Add-In. • Rely heavily on Excel for modeling and control analysis
Paper Machine Testing and Modeling • The sensor for the basis weight gives a measurement every 10 seconds. • Collect sampled data for all the process variables at 10 seconds intervals. • The sampling interval is small compared to our process dynamics we don’t need to bump the process and collect data at the exact time the basis weight sensor reports a data point.
Data Synchronization BW sensor sample interval of 10 s – Maximum out of sync by 5 s
Data Synchronization Fast Dynamics Slow dynamics BW sensor sample interval of 30 s – Maximum out of sync by 15 s
Sample Bump Test Sampling interval of 10s T=10 seconds
Discrete Gain Lag Model • The process looks like a linear first order response so lets model it with a discrete gain lag model. • We know that for a first order discrete gain lag model X(t + T) = A*X(t) + K*(1-A)*U(t) or X(t) = A*X(t-T) + K*(1-A)*U(t-T) Where X is the output, U is the input, A is the lag factor, and K is the process gain (i.e. DX/DU). Sometimes you will see B in place of K(1-A). • We can calculate A by the formula A = exp(-T/t)
Data Analysis A = exp(-T/t) A = exp(-10/60) A = 0.85 B = K*(1-A) B = 0.5*(1-0.85) B = 0.075 X(t+10) = A*X(t) + B*U(t) X(t+10) = 0.85*X(t) + 0.075*U(t)
